Many times, sports have been at the leading edge of data analytics. The
book “Moneyball” was one of the first popular books to bring the basic
concepts behind data analytics and data science to the general audience.
Fantasy leagues, sabermetrics and even games like “Strat-O-Matic”
baseball and basketball provided an introduction into basic statistical
concepts.
And it now seems that sports, in this case the National Basketball
Association (NBA), are breaking new ground with another data analytics topic:
who owns the data? The National Basketball Players Association recently
banned NBA teams from using a player’s wearable data in contract
negotiations or other transactions (see “NBA Bans Teams From Using Wearable
Data In Contract Negotiations”).
Maybe after the bitter fights professional and college athletes had about
their “likeness” being used for advertising... (more)
[Opening Scene]: Billy Dean is pacing the office. He’s struggling to keep
his delivery trucks at full capacity and on the road. Random breakdowns,
unexpected employee absences, and unscheduled truck maintenance are impacting
bookings, revenues and ultimately customer satisfaction. He keeps hearing
from his business customers how they are leveraging data science to improve
their business operations. Billy Dean starts to wonder if data science can
help him. As he contemplates what data science can do for him, he slowly
drifts off to sleep, and visions of Data Science starts dancing in his
head…
[Poof! Suddenly Wizard Wei appears]: Hi, I’m your data science wizard to
help alleviate your data science concerns. I don’t understand why folks try
to make the data science discussion complicated. Let’s start simple with a
simple definition of data science:
Data science is a... (more)
For a recent University of San Francisco MBA class, I wanted to put my
students in a challenging situation where they would be forced to make
difficult data science trade-offs between gathering data, preparing the data
and performing the actual analysis.
The purpose of the exercise was to test their ability to “think like a data
scientist” with respect to identifying and quantifying variables that might
be better predictors of performance. The exercise would require them to:
Set up a basic analytic environment Gather and organize different data
sources Explore the data using different visualization techniques Create and
test composite metrics by grouping and transforming base metrics Create a
score or analytic model that supports their recommendations
I gave them the links to 10 Warrior games (5 regulation wins, 3 overtime
losses and 2 regulation losses) as their star... (more)
Wikibon just released their “2017 Big Data Market Forecast.” How rosy
that forecast looks depends upon whether you look at Big Data as yet another
technology exercise, or if you look at Big Data as a business discipline that
organizations can unleash upon competitors and new market opportunities. To
quote the research:
“The big data market is rapidly evolving. As we predicted, the focus on
infrastructure is giving way to a focus on use cases, applications, and
creating sustainable business value with big data capabilities.”
Leading organizations are in the process of transitioning the big data
conversation from “what technologies and architectures do we need?” to
“how effective is our organization at leveraging data and analytics to
power our business models?”
We developed the Big Data Business Model Maturity Index to help our clients
to answer that question; to be... (more)
Dell EMC Announce Azure Stack Hybrid Cloud Solution
Dell EMC have announced their Microsoft Azure Stack hybrid cloud platform
solutions. This announcement builds upon earlier statements of support and
intention by Dell EMC to be part of the Microsoft Azure Stack community. For
those of you who are not familiar, Azure Stack is an on premise extension of
Microsoft Azure public cloud.
What this means is that essentially you can have the Microsoft Azure
experience (or a subset of it) in your own data center or data
infrastructure, enabling cloud experiences and abilities at your own pace,
your own way with control. Learn more about Microsoft Azure Stack including
my experiences with and installing Technique Preview 3 (TP3) here.
What Is Azure Stack
Microsoft Azure Stack is an on-premise (e.g., in your own data center)
private (or hybrid when connected to Azure) cloud pl... (more)
Decisions Exercise: Identifying Where and How to Start the Big Data Journey
The recent deluge of rains in Northern California have flooded streets,
brought down trees and plugged storm sewers. As I was trying to make my way
around the neighborhood, I thought of a classroom exercise to help my MBA
students to identify the use cases upon which they could focus data and
analytics. In this exercise, I’m going to ask my students to pretend that
they have been hired by the city to “Optimize Street Maintenance” after
these rainstorms. In particular, the students need to address the following
questions:
Where and how do you start to address this initiative? What data might you
need to support this initiative?
These are classic questions that I hear all the time when I meet with clients
about their big data journeys. Let’s walk through how I’ll teach my
students to addres... (more)
I spend a lot of time helping organizations to “think like a data
scientist.” My book “Big Data MBA: Driving Business Strategies with Data
Science” has several chapters devoted to helping business leaders to
embrace the power of data scientist thinking. My Big Data MBA class at the
University of San Francisco School of Management focuses on teaching
tomorrow’s business executives the power of analytics and data science to
optimize key business processes, uncover new monetization opportunities and
create a more compelling, engaging customer and channel engagement.
However in working with our data science teams, I have come to realize that
we also need to address the other side of the data science equation; that we
need to teach the data scientists in order for them to think like business
executives. If the data science team cannot present the analytic results in a
w... (more)
One of the best parts of my job is talking to a wide variety of customers
across a wide variety of industries at a wide variety of different points on
their big data journey. I’ve recently had several customer engagements
where the client’s top business initiative is creating a Customer 360 View.
Danger, Will Robinson!! I think the Customer 360 View business initiative
is both dangerous and distracting; it is dangerous because it gives
organizations a false goal to pursue, and it is distracting because it
diverts the organization’s resources from more actionable and financially
rewarding business initiatives.
The Customer 360 View is a relic of the old-school Business Intelligence and
data warehousing days. Hate to be so harsh, but for many organizations,
Customer 360 View was created as an artificial goal for organizations that
could not move beyond the Busine... (more)
Well, my recent University of San Francisco research paper “Applying
Economic Concepts To Big Data To Determine The Financial Value Of The
Organization’s Data And Analytics Research Paper” has fueled some very
interesting conversations. Most excellent! That was one of its goals.
It is important for organizations to invest the time and effort to understand
the economic value of their data because data has a direct impact on an
organization’s financial investments and monetization
capabilities. However, calculating economic value of data (EvD) is very
difficult because:
Data does not have an innate fixed value, especially as compared to
traditional assets, and Using traditional accounting practices to calculate
EvD doesn’t accurately capture the financial and economic potential of the
data asset.
And in light of those points, let me share some thoughts that I probably... (more)
Big Data Model Maturity Discussion - What Are You Measuring?
“Maturity models” can be very useful. Every analyst firm and most vendors
have created some sort of maturity model. Not only can a maturity model
benchmark where you are with respect to your cohorts, but good maturity
models also provide a roadmap to help organizations advance along the
maturity model. But different maturity models measure different things, and
what the maturity model measures is critically important because you are what
you measure.
For example, a friend recently sent me the below cartoon about the “5
Stages of Data-Driven Marketing” (see Figure 1).
Figure 1: Five Stages of Data-Driven Marketing
Figure 1 measures how effective an organization is at leveraging data to
drive an organization’s marketing culture. In the case of Figure 1, it
conveys the organizational and cultural challenges ... (more)
A recent argument with folks whose intelligence I hold in high regard (like
Tom, Brandon, Wei, Anil, etc.) got me thinking about the following question:
What does it mean, as a vendor, to say that you support the Internet of
Things (IoT) from an analytics perspective?
I think the heart of that question really boils down to this:
What are the differences between big data (which is analyzing large amounts
of mostly human-generated data to support longer-duration use cases such as
predictive maintenance, capacity planning, customer 360 and revenue
protection) and IoT (which is aggregating and compressing massive amounts of
low latency / low duration / high volume machine-generated data coming from a
wide variety of sensors to support real-time use cases such as operational
optimization, real-time ad bidding, fraud detection, and security breach
detection)?
I don’t beli... (more)
Subscribe to EMC Journal Email News Alerts
Subscribe via RSS
